The present invention relates to the art of diagnostic imaging of small pulmonary nodules. In particular, the present invention is related to analyzing and manipulating computed tomography scans to: detect lung nodules and correlate a pair of segmented images of a lung nodule obtained at different times.
Lung cancer is the leading cause of cancer death in the United States. According to the American Cancer Society, there were approximately 169,400 new cases of lung cancer (90,200 among men and 79,200 among women) in the United States in the year 2002. About 154,900 lung cancer deaths were predicted for the same year, which accounts for 28% of all cancer deaths. Although survival rate of lung cancer is only 14%, results from the ELCAP project show that detection and treatment of lung cancer at early stages may improve this rate to 70%. C. I. Henschke, D. I: McCauley, D. F. Yankelevitz, D. P. Naidich, G. McGuinness, O. S. Miettinen, D. M. Libby, M. W. Pasmantier, J. Koizumi, N. K. Altorki, and J. P. Smith. “Early Lung Cancer Action Project: overall design and findings from baseline screening.” Lancet Jul. 10, 1999; 354(9173):99-105. Hence, lung cancer screening has recently received considerable attention.
In the screening process, radiologists analyze images of asymptomatic patients searching for a specific abnormality. Henschke et al reported that using low-dose CT as compared to chest radiography can improve the detection of small, non-calcified nodules at potentially more curable stage. Claudia. I. Henschke, D. P. Naidich, D. F. Yankelevitz, G. McGuinness, D. I. McCauley, J. P. Smith, D. M. Libby, M. W. Pasmantier, M. Vazquez, J. Koizumi, D. Flleder, N. K. Altorki, and O. S. Miettinen, “Early Lung Cancer Action Project: Initial Findings on Repeat Screening.” Cancer Jul. 1, 2001; 92(1):153-159. The introduction of Computed Tomography (CT) scanners, particularly scanners with helical capabilities, has increased the resolution of lung images and greatly increased the number of images per screening study that must be evaluated by the radiologist. The development of the computed tomography (CT) technology and post-processing algorithms has provided radiologists with a useful tool for diagnosing lung cancers at early stages. However, current CT systems have their inherent shortcomings in that the amount of chest CT images (data) that is generated from a single CT examination, which can range from 30 to over 300 slices depending on image resolution along the scan axial direction, becomes a huge hurdle for the radiologists to interpret. Accordingly, there is a constant need for the improvement and development of diagnostic tools for enabling a radiologist to review and interpret the vast amount of information that is obtained through a CT examination.
International Publication No. WO 01/78005 A2 discloses a system and method for three dimensional image rendering and analysis, and is incorporated herein by reference. The system performs a variety of tasks that aid a radiologist in interpreting the results of a CT examination.
U.S. patent application Ser. No. 10/245,782 discloses a system and method directed to diagnostic imaging of small pulmonary nodules, and is incorporated herein by reference. The application includes methods for detection and feature extraction for size characterization, and focuses on the analysis of small pulmonary nodules that are less than 1 centimeter in size, but is also suitable for larger nodules as well.
Radiologists generally fail to detect nodules primarily due to interpretation and oversight error. The use of a computer aided detection (CAD) systems avoids these human related errors to tremendously improve diagnostic accuracy. M. Fiebich, C. Wietholt, B. C. Renger, S. G Armato, K. R. Hoffmann, D. Wormanns and S. Diederich, “Automatic detection of pulmonary nodules in low-dose screening thoracic CT ex-aminations”, SPIE, vol. 3661, pp 1434-1439, 1999; S. G Armato, 111, F. Li, M. L. Giger, “Performance of Automated CT Lung Nodule Detection on Missed Cancers”, scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001; F. Li, S. Sone, H. Abe, H. M MacMahon, S. G Armato, K. Doi, “Missed Lung Cancers in low-dose Helical CT Screening Obtained from a General Population”, scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001; C. L Novak, D. P Naidich, L. Fan, J. Qian, J. P. Ko, A. N Rubinowitz, “Improving Radiologists' Confidence of Interpreting Low-dose Multidetector Lung CT screening Studies Using an Interactive CAD system”, Scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001. CAD systems perform automated nodule detection in addition to providing useful visualization tools for the radiologists.
Novak et al reported an improvement in detection of potential nodules from 22 to 77% with the availability of an interactive CAD (ICAD) system for the radiologist. C. L Novak, D. P Naidich, L. Fan, J. Qian, J. P. Ko, A. N Rubinowitz, “Improving Radiologists' Confidence of Interpreting Low-dose Multidetector Lung CT screening Studies Using an Interactive CAD system”, Scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001. Potential nodules were identified and rated with and without the ICAD tools for nodule interpretation. They concluded from their results that the interactive CAD systems significantly increase radiologists confidence when interpreting CT screening studies. In another study, Aramato et al reported that 78% of nodules missed during visual interpretation were detected by their automated method. 72% of the missed nodules were due to oversight error, and the rest were due to interpretation error. S. G Armato, 111, F. Li, M. L. Giger, “Performance of Automated CT Lung Nodule Detection on Missed Cancers”, scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001. All the nodules missed due to oversight error were detected by their CAD system whereas 70% of the nodules missed due to interpretation error were detected. Li et al also showed that 78% of missed nodules were detected by their computerized scheme. F. Li, S. Sone, H. Abe, H. M MacMahon, S. G Armato, K. Doi, “Missed Lung Cancers in low-dose Helical CT Screening Obtained from a General Population”, scientific paper presentation, RSNA 87th scientific assembly and annual meeting, Nov. 25-30, 2001. According to them, the main reason for detection errors were difficulty in detection due to small size and low intensity, oversight due to adjacent or overlapping pulmonary vessels or fissures and lack of attention to relatively obvious nodules adjacent to the pulmonary hilum. In a related study, Fiebich et al reported a 15% improvement in detection sensitivity using their CAD system in addition the conventional film reading procedure. M. Fiebich, C. Wietholt, B. C. Renger, S. G Armato, K. R. Hoffmann, D. Wormanns and S. Diederich, “Automatic detection of pulmonary nodules in low-dose screening thoracic CT examinations”, SPIE, vol. 3661, pp 1434-1439, 1999.
The evolution of CT scanner technology has played an important role in the development of detection algorithms. Early low resolution whole lung CT scans had a slice thickness of 5-10 mm with a 0.5-0.6 mm in-plane resolution. Because of this low axial resolution, early computer detection algorithms were based entirely on two dimensional (slice-by-slice) image analysis techniques. Currently, multi-slice helical scanners with better axial resolution are widely available. This improvement in axial resolution permits three dimensional image analysis techniques which can detect smaller nodules.
Recent research on pulmonary nodule detection has focused on 3D region identification and feature extraction procedures followed by rule-based classification. Aramato et al implemented a computerized scheme that uses 2D and 3D extracted features from regions identified by multiple level gray-level thresholding. S. G. Armato III, M. L. Giger, J. T Blackburn, K. Doi, H. MacMahon, “Three-dimensional approach to lung nodule detection in helical CT”, SPIE, vol. 3661, pp. 553-559, 1999. In this paper, they used a rolling ball algorithm to avoid missing nodules attached to the pleural surface. They reported an operating point of 85% sensitivity and 89% specificity indicating an overall sensitivity of 70% with an average of three false-positive per slice. Similarly, 2D and 3D geometrical features have been used by Gurcan et al in their detection algorithm. M. K. Gurcan, N. Petrick, B. Sahiner, H. P. Chan, P. N. Cascade, E. A. Kazerooni, L. M. Hadjiiski, “Computerized lung nodule detection on thoracic CT images: combined rule-based and statistical classifier for false positive reduction”, SPIE, Vol. 4322, pp 686-692, 2001. They reported a 84% detection rate with 1.75 FPs per slice detection results tested on 17 patients with a total of 31 lung nodules. Fan et al implemented an adaptive 3D region growing algorithm followed by a classification scheme that makes use of geometric features such as diameter, volume, sphericity, mean intensity value and standard deviation of intensity. L. Fan, C. Novak, J. Qian, G. Kohl and D. Naidich, “Automatic Detection of Lung Nodules from Multi-Slice Low-Dose CT Images”, SPIE, vol. 4322, pp 1828-1835, 2001. This algorithm only detects nodules with very small vasculature connections and no large solid structure attachment. Toshioka et al tested their detection algorithm which on 450 cases (15,750 images). S. Toshioka, K. Kanazawa, N. Niki, H. Satoh, H. Ohmatsu, K. Eguchi, et al, “Computer aided diagnosis system for lung cancer based on helical CT images”, SPIE, vol. 3034, pp 975-984, 1997. Compared with image interpretation by 3 Radiologists, CAD detected all tumors identified as highly probable with 5 false negatives (4 of which represented tumors less than 5 mm in size) and 11 false positive cases (ranging from 2.4/case for “high probability” nodules to 7.2/case for “suspicious” nodules).
Lee et al used a template matching technique based on a genetic algorithm to detect nodules. Different templates were generated for nodules with and without an attachment to the pleural surface. Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT images Based on an Improved Template-Matching Technique”, IEEE Transactions on Medical Imaging, Vol. 20, No. 7, pp 595-604, 2001. Although, they developed an elegant mathematical model of a nodule, the algorithm resulted in a very high number of false positives (4.4 per slice) with 72% sensitivity. Other computer vision methods have also been explored for pulmonary nodule detection. Morphological analysis techniques have been utilized for detection of suspicious regions. H. Taguchi, Y. Kawata and N. Niki, H. Satoh, H. Ohmatsu, K. Eguchi, M. Kaneko and N. Moriyama, “Lung cancer detection based on helical CT images using curved surface morphology analysis”, SPIE, vol 3661, pp 1307-1313, 1999. Penedo et al have developed a computer aided detection system based on a two level artificial neural network. M. G. Penedo, M. J. Carreira, A. Mosquera, and D. Cabello, “Computer-aided diagnosis: a neural network based approach to lung nodule detection”, IEEE Transactions on Medical Imaging, vol. 17, no. 6, pp 872-879, 1998. The first network performs detection of suspicious regions, while the second one classifies the regions based on the curvature peak on all points in the suspicious region. They recorded 89%-96% sensitivity with 5-7 FPs per slice. Artificial neural networks' capabilities have also been used by Lo et el. S-C B. Lo, S-L A. Lou, J-S Lin, M. T. Freedman, M V. Chien and S. K. Mun, “Artificial convolution neural Network techniques and applications for lung nodule detection”, IEEE transactions on medical imaging, vol. 14, no. 4, pp 711-718, 1995. In their work, Lo et al used a convolution type neural network which recorded an 82% detection rate.
Object-based deformation techniques have been incorporated into detection systems. Lou et al used deformation techniques to differentiate lung nodules from blood vessels in their 3D CT lung nodule detection system. S. L Lou, C. L Chang, K. P Lin and T. Chen, “Object based deformation technique for 3-D CT lung nodule detection”, SPIE, vol 3661, pp 1544-1552.1999. This research did not address surface irregularities that occur in nodules with significant vasculature connections. Knowledge based techniques have also been used in recent research. Works of Erberich et al and Brown et al can be cited in this regard. S. G. Erberich, K. S. Song, H. Arakawa, H. K Huang, R. Webb, K. S. Hoo, B. W. Loo, “knowledge based lung nodule detection from helical CT”, RSNA 1997 annual meeting; M. S. Brown and M. F. McNitt-Gray, “Method for segmenting chest CT image data using an anatomical model: preliminary results”, SPIE, vol-16, no. 6, pp 828-839, 1997. Erberich et al used rule based tree to classify candidates generated using gradient Hough transformation. Detection performance statistical results were not reported in their paper. Brown et al developed a multipurpose modular knowledge based system. They demonstrated nodule detection application using this modular architecture.
A review of the prior art indicates that progress has been made on developing automated detection programs for lung nodules in helical CT scans. However, there is a large variation in performance, likely caused by the small data sets used in these studies. Much more effort is need to bring the performance of these computerized detection systems to level acceptable for clinical implementation. Most of the detection algorithms have been designed to detect a single type of a nodule (i.e. nodule with a small vessel connection). Nodules with significant vessel connections or attachment to large solid structure have been either reported as a missed or not considered in the detection performance evaluation. Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT images Based on an Improved Template-Matching Technique”, IEEE Transactions on Medical Imaging, Vol. 20, No. 7, pp 595-604, 2001; L. Fan, C. Novak, J. Qian, G. Kohl and D. Naidich, “Automatic Detection of Lung Nodules from Multi-Slice Low-Dose CT Images”, SPIE, vol. 4322, pp 1828-1835, 2001; M. Fiebich, C. Wietholt, B. C. Renger, S. G Armato, K. R. Hoffmann, D. Wormanns and S. Diederich, “Automatic detection of pulmonary nodules in low-dose screening thoracic CT ex-aminations”, SPIE, vol. 3661, pp 1434-1439, 1999. Accordingly, there is a great need for an algorithm which detects nodules with or without attachments to large solid structures with fewer false positives.
One predictor of malignancy of a pulmonary nodule in a CT image is the change in volume of the nodule. The change in volume can be measured as percent volume change or a doubling time. To obtain these measurements, two high-resolution CT scans, separated by a few months, are taken of the nodule. The nodules are segmented from the CT images and the percent volume change or doubling time is calculated using the segmented nodule volumes. The accuracy of the change in volume measurement is dependent on the consistency of the segmentations of the nodule in the two images. In the extreme case, a missegmentation of one of the nodules may adversely affect the malignancy predictor by moving the doubling time measurement above or below the threshold for malignancy.
There has been some work on tracking the change of pulmonary nodules in CT images. In Kawata et al, the pulmonary nodules are registered together using rigid-body registration and affine registration at two different stages. Y. Kawata, N. Niki, H. Ohmatsu, M. Kusumoto, R. Kakinuma, K. Mori, N. Nishiyama, K. Eguchi, M. Kaneko, and N. Moriyama. Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images. In Medical Imaging 2000: Image Processing, Proceedings of SPIE, volume 3979, pages 86-96, 2000. The nodules are segmented using a 3-D deformable surface model and curvature features are calculated to track the temporal evolution of the nodule. This work was extended by Kawata et al, by adding an additional 3-D non-rigid deformable registration stage and the analysis was performed using a displacement field to quantify the areas of nodule growth over time. Y. Kawata, N. Niki, H. Ohmatsu, M. Kusumoto, R. Kakinuma, K. Mori, N. Nishiyama, K. Eguchi, M. Kaneko, and N. Moriyama. Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images. In M. Sonka and K. M. Hanson, editors, Medical Imaging 2001: Image Processing, Proceedings of SPIE, volume 4322, pages 1890-1901, 2001. In Reeves et al, a method was introduced to estimate the growth of a nodule without the explicit use of segmentation. A. P. Reeves, W. J. Kostis, D. F. Yankelevitz, and C. I. Henschke. Analysis of small pulmonary nodules without explicit segmentation of CT images. Radiology, 217P:243-244, November 2000. The pulmonary nodules are registered using translation and the doubling time is calculated from the gaussian-weighted regions-of-interest. In Kostis et al, and Reeves et al, a segmentation method based on thresholding and morphological filtering is discussed. W. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke. Three-dimensional segmentation of solitary pulmonary nodules from helical CT scans. In H. U. Lemke, M. W. Vannier, K. Inamura, and A. G. Farman, editors, Proceedings of Computer Assisted Radiology and Surgery (CARS '99), pages 203-207. Elsevier Science, June 1999; A. P. Reeves and W. J. Kostis. Computer-aided diagnosis of small pulmonary nodules. Seminars in Ultrasound, CT, and MRI, 21(2):116-128, April 2000. From the nodule segmentation, the volume of the nodule can be easily calculated and the doubling-time can be determined.